Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations1131
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory756.6 KiB
Average record size in memory685.0 B

Variable types

Text2
Categorical6
Numeric10
DateTime2

Alerts

user_id has unique values Unique
start_time has unique values Unique
duration_hours has unique values Unique
energy_consumed_kwh has unique values Unique
charging_cost_usd has unique values Unique
charging_rate_kw has unique values Unique
soc_start_percent has unique values Unique
soc_end_percent has unique values Unique
distance_driven_km has unique values Unique
temperature_c has unique values Unique
vehicle_age_years has 126 (11.1%) zeros Zeros

Reproduction

Analysis started2024-12-21 14:57:09.470769
Analysis finished2024-12-21 14:57:12.954580
Duration3.48 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

user_id
Text

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size63.3 KiB
2024-12-21T09:57:13.052502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.1556145
Min length6

Characters and Unicode

Total characters9224
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1131 ?
Unique (%)100.0%

Sample

1st rowUser_1
2nd rowUser_2
3rd rowUser_3
4th rowUser_4
5th rowUser_5
ValueCountFrequency (%)
user_1 1
 
0.1%
user_11 1
 
0.1%
user_5 1
 
0.1%
user_6 1
 
0.1%
user_7 1
 
0.1%
user_8 1
 
0.1%
user_37 1
 
0.1%
user_9 1
 
0.1%
user_12 1
 
0.1%
user_3 1
 
0.1%
Other values (1121) 1121
99.1%
2024-12-21T09:57:13.264798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1131
12.3%
s 1131
12.3%
e 1131
12.3%
r 1131
12.3%
_ 1131
12.3%
1 675
7.3%
2 398
 
4.3%
3 326
 
3.5%
4 319
 
3.5%
0 312
 
3.4%
Other values (5) 1539
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1131
12.3%
s 1131
12.3%
e 1131
12.3%
r 1131
12.3%
_ 1131
12.3%
1 675
7.3%
2 398
 
4.3%
3 326
 
3.5%
4 319
 
3.5%
0 312
 
3.4%
Other values (5) 1539
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1131
12.3%
s 1131
12.3%
e 1131
12.3%
r 1131
12.3%
_ 1131
12.3%
1 675
7.3%
2 398
 
4.3%
3 326
 
3.5%
4 319
 
3.5%
0 312
 
3.4%
Other values (5) 1539
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1131
12.3%
s 1131
12.3%
e 1131
12.3%
r 1131
12.3%
_ 1131
12.3%
1 675
7.3%
2 398
 
4.3%
3 326
 
3.5%
4 319
 
3.5%
0 312
 
3.4%
Other values (5) 1539
16.7%

user_type
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size70.0 KiB
Commuter
404 
Long-Distance Traveler
381 
Casual Driver
346 

Length

Max length22
Median length13
Mean length14.2458
Min length8

Characters and Unicode

Total characters16112
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCommuter
2nd rowCasual Driver
3rd rowCommuter
4th rowLong-Distance Traveler
5th rowLong-Distance Traveler

Common Values

ValueCountFrequency (%)
Commuter 404
35.7%
Long-Distance Traveler 381
33.7%
Casual Driver 346
30.6%

Length

2024-12-21T09:57:13.328630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T09:57:13.362138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
commuter 404
21.7%
long-distance 381
20.5%
traveler 381
20.5%
casual 346
18.6%
driver 346
18.6%

Most occurring characters

ValueCountFrequency (%)
e 1893
 
11.7%
r 1858
 
11.5%
a 1454
 
9.0%
m 808
 
5.0%
t 785
 
4.9%
o 785
 
4.9%
n 762
 
4.7%
C 750
 
4.7%
u 750
 
4.7%
s 727
 
4.5%
Other values (10) 5540
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1893
 
11.7%
r 1858
 
11.5%
a 1454
 
9.0%
m 808
 
5.0%
t 785
 
4.9%
o 785
 
4.9%
n 762
 
4.7%
C 750
 
4.7%
u 750
 
4.7%
s 727
 
4.5%
Other values (10) 5540
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1893
 
11.7%
r 1858
 
11.5%
a 1454
 
9.0%
m 808
 
5.0%
t 785
 
4.9%
o 785
 
4.9%
n 762
 
4.7%
C 750
 
4.7%
u 750
 
4.7%
s 727
 
4.5%
Other values (10) 5540
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1893
 
11.7%
r 1858
 
11.5%
a 1454
 
9.0%
m 808
 
5.0%
t 785
 
4.9%
o 785
 
4.9%
n 762
 
4.7%
C 750
 
4.7%
u 750
 
4.7%
s 727
 
4.5%
Other values (10) 5540
34.4%

vehicle_model
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size65.8 KiB
Tesla Model 3
244 
Nissan Leaf
227 
BMW i3
223 
Chevy Bolt
219 
Hyundai Kona
218 

Length

Max length13
Median length11
Mean length10.444739
Min length6

Characters and Unicode

Total characters11813
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBMW i3
2nd rowHyundai Kona
3rd rowChevy Bolt
4th rowHyundai Kona
5th rowHyundai Kona

Common Values

ValueCountFrequency (%)
Tesla Model 3 244
21.6%
Nissan Leaf 227
20.1%
BMW i3 223
19.7%
Chevy Bolt 219
19.4%
Hyundai Kona 218
19.3%

Length

2024-12-21T09:57:13.399344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T09:57:13.431203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tesla 244
9.7%
model 244
9.7%
3 244
9.7%
nissan 227
9.1%
leaf 227
9.1%
bmw 223
8.9%
i3 223
8.9%
chevy 219
8.7%
bolt 219
8.7%
hyundai 218
8.7%

Most occurring characters

ValueCountFrequency (%)
1375
 
11.6%
a 1134
 
9.6%
e 934
 
7.9%
l 707
 
6.0%
s 698
 
5.9%
o 681
 
5.8%
i 668
 
5.7%
n 663
 
5.6%
M 467
 
4.0%
3 467
 
4.0%
Other values (15) 4019
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1375
 
11.6%
a 1134
 
9.6%
e 934
 
7.9%
l 707
 
6.0%
s 698
 
5.9%
o 681
 
5.8%
i 668
 
5.7%
n 663
 
5.6%
M 467
 
4.0%
3 467
 
4.0%
Other values (15) 4019
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1375
 
11.6%
a 1134
 
9.6%
e 934
 
7.9%
l 707
 
6.0%
s 698
 
5.9%
o 681
 
5.8%
i 668
 
5.7%
n 663
 
5.6%
M 467
 
4.0%
3 467
 
4.0%
Other values (15) 4019
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1375
 
11.6%
a 1134
 
9.6%
e 934
 
7.9%
l 707
 
6.0%
s 698
 
5.9%
o 681
 
5.8%
i 668
 
5.7%
n 663
 
5.6%
M 467
 
4.0%
3 467
 
4.0%
Other values (15) 4019
34.0%

vehicle_age_years
Real number (ℝ)

Zeros 

Distinct103
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6042266
Minimum0
Maximum11.688592
Zeros126
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-12-21T09:57:13.471021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile7
Maximum11.688592
Range11.688592
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3240896
Coefficient of variation (CV)0.64482338
Kurtosis-1.0295344
Mean3.6042266
Median Absolute Deviation (MAD)2
Skewness0.04164549
Sum4076.3802
Variance5.4013923
MonotonicityNot monotonic
2024-12-21T09:57:13.568007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 142
12.6%
5 138
12.2%
6 138
12.2%
3 132
11.7%
0 126
11.1%
2 122
10.8%
4 119
10.5%
1 119
10.5%
5.404853321 1
 
0.1%
0.3556527874 1
 
0.1%
Other values (93) 93
8.2%
ValueCountFrequency (%)
0 126
11.1%
0.020155741 1
 
0.1%
0.08859825775 1
 
0.1%
0.1900469257 1
 
0.1%
0.2884081107 1
 
0.1%
0.3556527874 1
 
0.1%
0.3612151658 1
 
0.1%
0.4616615532 1
 
0.1%
0.6749470228 1
 
0.1%
0.6898866541 1
 
0.1%
ValueCountFrequency (%)
11.68859247 1
 
0.1%
10.63457441 1
 
0.1%
10.54723654 1
 
0.1%
9.076121184 1
 
0.1%
8.018665733 1
 
0.1%
7.690884188 1
 
0.1%
7.51530869 1
 
0.1%
7.197041544 1
 
0.1%
7 142
12.6%
6.817970665 1
 
0.1%
Distinct446
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Memory size66.1 KiB
2024-12-21T09:57:13.662106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.770115
Min length9

Characters and Unicode

Total characters12181
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)10.0%

Sample

1st rowStation_391
2nd rowStation_428
3rd rowStation_181
4th rowStation_327
5th rowStation_108
ValueCountFrequency (%)
station_108 8
 
0.7%
station_17 7
 
0.6%
station_461 7
 
0.6%
station_44 6
 
0.5%
station_181 6
 
0.5%
station_402 6
 
0.5%
station_211 6
 
0.5%
station_57 6
 
0.5%
station_140 6
 
0.5%
station_69 6
 
0.5%
Other values (436) 1067
94.3%
2024-12-21T09:57:13.806541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2262
18.6%
S 1131
9.3%
a 1131
9.3%
i 1131
9.3%
o 1131
9.3%
n 1131
9.3%
_ 1131
9.3%
1 475
 
3.9%
4 460
 
3.8%
2 445
 
3.7%
Other values (7) 1753
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2262
18.6%
S 1131
9.3%
a 1131
9.3%
i 1131
9.3%
o 1131
9.3%
n 1131
9.3%
_ 1131
9.3%
1 475
 
3.9%
4 460
 
3.8%
2 445
 
3.7%
Other values (7) 1753
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2262
18.6%
S 1131
9.3%
a 1131
9.3%
i 1131
9.3%
o 1131
9.3%
n 1131
9.3%
_ 1131
9.3%
1 475
 
3.9%
4 460
 
3.8%
2 445
 
3.7%
Other values (7) 1753
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2262
18.6%
S 1131
9.3%
a 1131
9.3%
i 1131
9.3%
o 1131
9.3%
n 1131
9.3%
_ 1131
9.3%
1 475
 
3.9%
4 460
 
3.8%
2 445
 
3.7%
Other values (7) 1753
14.4%

station_location
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size64.5 KiB
Los Angeles
276 
Houston
239 
New York
215 
San Francisco
202 
Chicago
199 

Length

Max length13
Median length11
Mean length9.2378426
Min length7

Characters and Unicode

Total characters10448
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouston
2nd rowSan Francisco
3rd rowSan Francisco
4th rowHouston
5th rowLos Angeles

Common Values

ValueCountFrequency (%)
Los Angeles 276
24.4%
Houston 239
21.1%
New York 215
19.0%
San Francisco 202
17.9%
Chicago 199
17.6%

Length

2024-12-21T09:57:13.859052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T09:57:13.890724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
los 276
15.1%
angeles 276
15.1%
houston 239
13.1%
new 215
11.8%
york 215
11.8%
san 202
11.1%
francisco 202
11.1%
chicago 199
10.9%

Most occurring characters

ValueCountFrequency (%)
o 1370
13.1%
s 993
 
9.5%
n 919
 
8.8%
e 767
 
7.3%
693
 
6.6%
a 603
 
5.8%
c 603
 
5.8%
g 475
 
4.5%
r 417
 
4.0%
i 401
 
3.8%
Other values (14) 3207
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1370
13.1%
s 993
 
9.5%
n 919
 
8.8%
e 767
 
7.3%
693
 
6.6%
a 603
 
5.8%
c 603
 
5.8%
g 475
 
4.5%
r 417
 
4.0%
i 401
 
3.8%
Other values (14) 3207
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1370
13.1%
s 993
 
9.5%
n 919
 
8.8%
e 767
 
7.3%
693
 
6.6%
a 603
 
5.8%
c 603
 
5.8%
g 475
 
4.5%
r 417
 
4.0%
i 401
 
3.8%
Other values (14) 3207
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1370
13.1%
s 993
 
9.5%
n 919
 
8.8%
e 767
 
7.3%
693
 
6.6%
a 603
 
5.8%
c 603
 
5.8%
g 475
 
4.5%
r 417
 
4.0%
i 401
 
3.8%
Other values (14) 3207
30.7%

charger_type
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size65.2 KiB
DC Fast Charger
411 
Level 1
363 
Level 2
357 

Length

Max length15
Median length7
Mean length9.9071618
Min length7

Characters and Unicode

Total characters11205
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDC Fast Charger
2nd rowLevel 1
3rd rowLevel 2
4th rowLevel 1
5th rowLevel 1

Common Values

ValueCountFrequency (%)
DC Fast Charger 411
36.3%
Level 1 363
32.1%
Level 2 357
31.6%

Length

2024-12-21T09:57:13.929446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T09:57:13.960449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
level 720
26.9%
dc 411
15.4%
fast 411
15.4%
charger 411
15.4%
1 363
13.6%
2 357
13.4%

Most occurring characters

ValueCountFrequency (%)
e 1851
16.5%
1542
13.8%
C 822
 
7.3%
a 822
 
7.3%
r 822
 
7.3%
L 720
 
6.4%
v 720
 
6.4%
l 720
 
6.4%
D 411
 
3.7%
F 411
 
3.7%
Other values (6) 2364
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1851
16.5%
1542
13.8%
C 822
 
7.3%
a 822
 
7.3%
r 822
 
7.3%
L 720
 
6.4%
v 720
 
6.4%
l 720
 
6.4%
D 411
 
3.7%
F 411
 
3.7%
Other values (6) 2364
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1851
16.5%
1542
13.8%
C 822
 
7.3%
a 822
 
7.3%
r 822
 
7.3%
L 720
 
6.4%
v 720
 
6.4%
l 720
 
6.4%
D 411
 
3.7%
F 411
 
3.7%
Other values (6) 2364
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1851
16.5%
1542
13.8%
C 822
 
7.3%
a 822
 
7.3%
r 822
 
7.3%
L 720
 
6.4%
v 720
 
6.4%
l 720
 
6.4%
D 411
 
3.7%
F 411
 
3.7%
Other values (6) 2364
21.1%

start_time
Date

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Minimum2024-01-01 00:00:00
Maximum2024-02-24 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-21T09:57:13.996276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:14.038438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1121
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Minimum2024-01-01 00:39:00
Maximum2024-02-24 23:56:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-21T09:57:14.078695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:14.121211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

duration_hours
Real number (ℝ)

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3031773
Minimum0.095314417
Maximum7.6351448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-12-21T09:57:14.160362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.095314417
5-th percentile0.67759528
Q11.4252811
median2.3126751
Q33.1459982
95-th percentile3.8629289
Maximum7.6351448
Range7.5398303
Interquartile range (IQR)1.7207171

Descriptive statistics

Standard deviation1.0658784
Coefficient of variation (CV)0.46278609
Kurtosis0.33507869
Mean2.3031773
Median Absolute Deviation (MAD)0.85703915
Skewness0.34347727
Sum2604.8935
Variance1.1360968
MonotonicityNot monotonic
2024-12-21T09:57:14.202517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5913634254 1
 
0.1%
2.572225077 1
 
0.1%
3.308261928 1
 
0.1%
2.012360141 1
 
0.1%
0.9497561026 1
 
0.1%
1.967714479 1
 
0.1%
0.7713119843 1
 
0.1%
2.296980268 1
 
0.1%
2.917826685 1
 
0.1%
2.879571205 1
 
0.1%
Other values (1121) 1121
99.1%
ValueCountFrequency (%)
0.09531441655 1
0.1%
0.1400299896 1
0.1%
0.1543006839 1
0.1%
0.1653938787 1
0.1%
0.182479567 1
0.1%
0.2066308831 1
0.1%
0.3599172409 1
0.1%
0.504601217 1
0.1%
0.512378183 1
0.1%
0.513006351 1
0.1%
ValueCountFrequency (%)
7.635144759 1
0.1%
6.773095327 1
0.1%
6.75915168 1
0.1%
6.494007043 1
0.1%
6.17641656 1
0.1%
5.945571288 1
0.1%
5.505495842 1
0.1%
5.444091596 1
0.1%
5.220359375 1
0.1%
5.211612226 1
0.1%

energy_consumed_kwh
Real number (ℝ)

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.915668
Minimum0.045771842
Maximum152.23876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-12-21T09:57:14.242704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.045771842
5-th percentile8.2585113
Q124.248936
median42.865611
Q361.544055
95-th percentile76.538511
Maximum152.23876
Range152.19299
Interquartile range (IQR)37.295118

Descriptive statistics

Standard deviation22.201286
Coefficient of variation (CV)0.51732356
Kurtosis-0.49399116
Mean42.915668
Median Absolute Deviation (MAD)18.655149
Skewness0.11567182
Sum48537.621
Variance492.8971
MonotonicityNot monotonic
2024-12-21T09:57:14.283111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.71234573 1
 
0.1%
58.11274414 1
 
0.1%
77.45420996 1
 
0.1%
36.26259614 1
 
0.1%
51.08539267 1
 
0.1%
9.047732107 1
 
0.1%
28.78049141 1
 
0.1%
56.52037668 1
 
0.1%
72.95818105 1
 
0.1%
57.67538256 1
 
0.1%
Other values (1121) 1121
99.1%
ValueCountFrequency (%)
0.04577184228 1
0.1%
0.121143295 1
0.1%
1.330225739 1
0.1%
1.754520234 1
0.1%
4.288320356 1
0.1%
5.013889647 1
0.1%
5.144893572 1
0.1%
5.163807088 1
0.1%
5.200112601 1
0.1%
5.337836074 1
0.1%
ValueCountFrequency (%)
152.238758 1
0.1%
127.7574744 1
0.1%
103.8126056 1
0.1%
97.66776539 1
0.1%
94.36532941 1
0.1%
90.23550358 1
0.1%
85.03985431 1
0.1%
80.72941928 1
0.1%
79.97267191 1
0.1%
79.96222628 1
0.1%

charging_cost_usd
Real number (ℝ)

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.488351
Minimum0.30708545
Maximum69.407743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-12-21T09:57:14.322554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.30708545
5-th percentile7.0922303
Q113.133925
median21.828088
Q331.675804
95-th percentile38.353076
Maximum69.407743
Range69.100658
Interquartile range (IQR)18.541879

Descriptive statistics

Standard deviation10.792504
Coefficient of variation (CV)0.4799153
Kurtosis-0.46482219
Mean22.488351
Median Absolute Deviation (MAD)9.2619716
Skewness0.28000199
Sum25434.325
Variance116.47814
MonotonicityNot monotonic
2024-12-21T09:57:14.363034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.08771679 1
 
0.1%
36.43538614 1
 
0.1%
5.691471522 1
 
0.1%
35.14611587 1
 
0.1%
7.181392583 1
 
0.1%
25.26991752 1
 
0.1%
27.52125904 1
 
0.1%
9.434171312 1
 
0.1%
20.51806392 1
 
0.1%
33.67417017 1
 
0.1%
Other values (1121) 1121
99.1%
ValueCountFrequency (%)
0.3070854525 1
0.1%
0.6713465052 1
0.1%
1.640802843 1
0.1%
3.059041507 1
0.1%
3.668584746 1
0.1%
3.707066616 1
0.1%
5.043208361 1
0.1%
5.056096912 1
0.1%
5.06404815 1
0.1%
5.06780582 1
0.1%
ValueCountFrequency (%)
69.40774319 1
0.1%
68.93168829 1
0.1%
60.40252537 1
0.1%
58.74667669 1
0.1%
54.89298253 1
0.1%
49.13746988 1
0.1%
46.27027201 1
0.1%
46.17291744 1
0.1%
46.05917303 1
0.1%
45.04426936 1
0.1%

charging_rate_kw
Real number (ℝ)

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.014166
Minimum1.4725491
Maximum97.342255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-12-21T09:57:14.404242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.4725491
5-th percentile5.3525974
Q113.949809
median25.838488
Q337.508677
95-th percentile47.734818
Maximum97.342255
Range95.869706
Interquartile range (IQR)23.558868

Descriptive statistics

Standard deviation14.010292
Coefficient of variation (CV)0.53856394
Kurtosis-0.48585441
Mean26.014166
Median Absolute Deviation (MAD)11.738802
Skewness0.2258641
Sum29422.022
Variance196.28827
MonotonicityNot monotonic
2024-12-21T09:57:14.444420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.38918057 1
 
0.1%
43.4773024 1
 
0.1%
29.41340669 1
 
0.1%
38.149082 1
 
0.1%
25.83848785 1
 
0.1%
40.91079821 1
 
0.1%
7.446768634 1
 
0.1%
12.8885881 1
 
0.1%
29.99045483 1
 
0.1%
46.31316025 1
 
0.1%
Other values (1121) 1121
99.1%
ValueCountFrequency (%)
1.472549139 1
0.1%
1.654937068 1
0.1%
1.722296154 1
0.1%
2.246134508 1
0.1%
2.535237862 1
0.1%
2.560943434 1
0.1%
2.63696679 1
0.1%
2.826146252 1
0.1%
3.023485282 1
0.1%
3.508161487 1
0.1%
ValueCountFrequency (%)
97.3422547 1
0.1%
71.62104096 1
0.1%
68.54260187 1
0.1%
67.93467893 1
0.1%
63.21611838 1
0.1%
59.14854489 1
0.1%
57.09975217 1
0.1%
53.1747828 1
0.1%
51.36359325 1
0.1%
50.35062408 1
0.1%

soc_start_percent
Real number (ℝ)

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.230036
Minimum2.325959
Maximum125.08723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-12-21T09:57:14.482888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.325959
5-th percentile13.88316
Q127.661992
median48.947886
Q369.783816
95-th percentile87.184429
Maximum125.08723
Range122.76127
Interquartile range (IQR)42.121824

Descriptive statistics

Standard deviation24.170435
Coefficient of variation (CV)0.49096928
Kurtosis-1.01799
Mean49.230036
Median Absolute Deviation (MAD)21.18183
Skewness0.1506309
Sum55679.171
Variance584.20994
MonotonicityNot monotonic
2024-12-21T09:57:14.523781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.37157598 1
 
0.1%
74.63709319 1
 
0.1%
80.65255109 1
 
0.1%
58.31902031 1
 
0.1%
44.70187213 1
 
0.1%
86.31805713 1
 
0.1%
27.2394756 1
 
0.1%
31.16795633 1
 
0.1%
40.21207607 1
 
0.1%
67.4595416 1
 
0.1%
Other values (1121) 1121
99.1%
ValueCountFrequency (%)
2.325958995 1
0.1%
2.481152833 1
0.1%
6.854604444 1
0.1%
7.174271255 1
0.1%
7.520444727 1
0.1%
10.11577764 1
0.1%
10.28531461 1
0.1%
10.42770632 1
0.1%
10.67715373 1
0.1%
10.71439507 1
0.1%
ValueCountFrequency (%)
125.0872271 1
0.1%
121.8472807 1
0.1%
119.0511075 1
0.1%
117.656235 1
0.1%
109.2657943 1
0.1%
105.032224 1
0.1%
98.33279531 1
0.1%
96.11613714 1
0.1%
95.79218803 1
0.1%
94.43594757 1
0.1%

soc_end_percent
Real number (ℝ)

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.012917
Minimum7.6042245
Maximum177.70867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-12-21T09:57:14.562440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.6042245
5-th percentile51.726384
Q162.26446
median75.100944
Q388.24507
95-th percentile98.040603
Maximum177.70867
Range170.10444
Interquartile range (IQR)25.98061

Descriptive statistics

Standard deviation16.920463
Coefficient of variation (CV)0.22556732
Kurtosis2.3724029
Mean75.012917
Median Absolute Deviation (MAD)13.067632
Skewness0.24471215
Sum84839.609
Variance286.30207
MonotonicityNot monotonic
2024-12-21T09:57:14.601888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.11996244 1
 
0.1%
59.82731321 1
 
0.1%
90.08739344 1
 
0.1%
67.00704756 1
 
0.1%
82.6285391 1
 
0.1%
82.64673079 1
 
0.1%
94.99744371 1
 
0.1%
68.26436301 1
 
0.1%
62.269598 1
 
0.1%
64.97948546 1
 
0.1%
Other values (1121) 1121
99.1%
ValueCountFrequency (%)
7.604224498 1
0.1%
10.08007433 1
0.1%
14.98994632 1
0.1%
18.70034949 1
0.1%
18.83987639 1
0.1%
19.57180031 1
0.1%
22.27521585 1
0.1%
22.88625804 1
0.1%
23.03568088 1
0.1%
23.5484923 1
0.1%
ValueCountFrequency (%)
177.7086665 1
0.1%
159.9889032 1
0.1%
147.4921301 1
0.1%
146.8476439 1
0.1%
146.7594514 1
0.1%
139.8974081 1
0.1%
133.6294352 1
0.1%
116.0956569 1
0.1%
114.5256472 1
0.1%
114.167842 1
0.1%

time_of_day
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size62.0 KiB
Evening
311 
Morning
289 
Afternoon
268 
Night
263 

Length

Max length9
Median length7
Mean length7.0088417
Min length5

Characters and Unicode

Total characters7927
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvening
2nd rowMorning
3rd rowMorning
4th rowEvening
5th rowMorning

Common Values

ValueCountFrequency (%)
Evening 311
27.5%
Morning 289
25.6%
Afternoon 268
23.7%
Night 263
23.3%

Length

2024-12-21T09:57:14.698472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T09:57:14.730771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
evening 311
27.5%
morning 289
25.6%
afternoon 268
23.7%
night 263
23.3%

Most occurring characters

ValueCountFrequency (%)
n 1736
21.9%
i 863
10.9%
g 863
10.9%
o 825
10.4%
e 579
 
7.3%
r 557
 
7.0%
t 531
 
6.7%
E 311
 
3.9%
v 311
 
3.9%
M 289
 
3.6%
Other values (4) 1062
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1736
21.9%
i 863
10.9%
g 863
10.9%
o 825
10.4%
e 579
 
7.3%
r 557
 
7.0%
t 531
 
6.7%
E 311
 
3.9%
v 311
 
3.9%
M 289
 
3.6%
Other values (4) 1062
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1736
21.9%
i 863
10.9%
g 863
10.9%
o 825
10.4%
e 579
 
7.3%
r 557
 
7.0%
t 531
 
6.7%
E 311
 
3.9%
v 311
 
3.9%
M 289
 
3.6%
Other values (4) 1062
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1736
21.9%
i 863
10.9%
g 863
10.9%
o 825
10.4%
e 579
 
7.3%
r 557
 
7.0%
t 531
 
6.7%
E 311
 
3.9%
v 311
 
3.9%
M 289
 
3.6%
Other values (4) 1062
13.4%

day_of_week
Categorical

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
Tuesday
175 
Saturday
171 
Wednesday
167 
Sunday
167 
Friday
160 
Other values (2)
291 

Length

Max length9
Median length8
Mean length7.1335102
Min length6

Characters and Unicode

Total characters8068
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowMonday
3rd rowThursday
4th rowSaturday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Tuesday 175
15.5%
Saturday 171
15.1%
Wednesday 167
14.8%
Sunday 167
14.8%
Friday 160
14.1%
Monday 159
14.1%
Thursday 132
11.7%

Length

2024-12-21T09:57:14.766128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-21T09:57:14.800316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 175
15.5%
saturday 171
15.1%
wednesday 167
14.8%
sunday 167
14.8%
friday 160
14.1%
monday 159
14.1%
thursday 132
11.7%

Most occurring characters

ValueCountFrequency (%)
a 1302
16.1%
d 1298
16.1%
y 1131
14.0%
u 645
8.0%
e 509
 
6.3%
n 493
 
6.1%
s 474
 
5.9%
r 463
 
5.7%
S 338
 
4.2%
T 307
 
3.8%
Other values (7) 1108
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1302
16.1%
d 1298
16.1%
y 1131
14.0%
u 645
8.0%
e 509
 
6.3%
n 493
 
6.1%
s 474
 
5.9%
r 463
 
5.7%
S 338
 
4.2%
T 307
 
3.8%
Other values (7) 1108
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1302
16.1%
d 1298
16.1%
y 1131
14.0%
u 645
8.0%
e 509
 
6.3%
n 493
 
6.1%
s 474
 
5.9%
r 463
 
5.7%
S 338
 
4.2%
T 307
 
3.8%
Other values (7) 1108
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1302
16.1%
d 1298
16.1%
y 1131
14.0%
u 645
8.0%
e 509
 
6.3%
n 493
 
6.1%
s 474
 
5.9%
r 463
 
5.7%
S 338
 
4.2%
T 307
 
3.8%
Other values (7) 1108
13.7%

distance_driven_km
Real number (ℝ)

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.6631
Minimum1.8995379
Maximum398.36477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-12-21T09:57:14.843371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.8995379
5-th percentile23.54604
Q180.954993
median152.25752
Q3225.46963
95-th percentile286.43105
Maximum398.36477
Range396.46524
Interquartile range (IQR)144.51463

Descriptive statistics

Standard deviation85.549751
Coefficient of variation (CV)0.55673581
Kurtosis-1.1263777
Mean153.6631
Median Absolute Deviation (MAD)72.302401
Skewness0.085544269
Sum173792.97
Variance7318.7598
MonotonicityNot monotonic
2024-12-21T09:57:14.888208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
293.6021106 1
 
0.1%
293.1202476 1
 
0.1%
238.8650098 1
 
0.1%
85.38239414 1
 
0.1%
121.4352927 1
 
0.1%
286.8427866 1
 
0.1%
162.4659355 1
 
0.1%
114.1895795 1
 
0.1%
256.2701113 1
 
0.1%
124.0786461 1
 
0.1%
Other values (1121) 1121
99.1%
ValueCountFrequency (%)
1.899537885 1
0.1%
2.908369295 1
0.1%
3.824527427 1
0.1%
7.619894391 1
0.1%
10.02829517 1
0.1%
10.87998409 1
0.1%
10.89843061 1
0.1%
10.93101298 1
0.1%
11.21551961 1
0.1%
11.60122874 1
0.1%
ValueCountFrequency (%)
398.3647747 1
0.1%
384.6905686 1
0.1%
369.8535236 1
0.1%
327.3930934 1
0.1%
324.5247972 1
0.1%
311.6304081 1
0.1%
302.8524897 1
0.1%
299.7598782 1
0.1%
299.6629508 1
0.1%
299.5639342 1
0.1%

temperature_c
Real number (ℝ)

Unique 

Distinct1131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.30578
Minimum-10.72477
Maximum73.169588
Zeros0
Zeros (%)0.0%
Negative216
Negative (%)19.1%
Memory size9.0 KiB
2024-12-21T09:57:14.928668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-10.72477
5-th percentile-7.4702502
Q13.0094977
median14.641853
Q327.824244
95-th percentile37.899431
Maximum73.169588
Range83.894358
Interquartile range (IQR)24.814746

Descriptive statistics

Standard deviation14.751266
Coefficient of variation (CV)0.96377092
Kurtosis-0.78159056
Mean15.30578
Median Absolute Deviation (MAD)12.367868
Skewness0.16495472
Sum17310.837
Variance217.59984
MonotonicityNot monotonic
2024-12-21T09:57:14.964191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.94795306 1
 
0.1%
24.13446895 1
 
0.1%
2.24905517 1
 
0.1%
-0.495076603 1
 
0.1%
16.95309452 1
 
0.1%
37.03618549 1
 
0.1%
5.291260873 1
 
0.1%
19.10315318 1
 
0.1%
12.98738853 1
 
0.1%
3.261647704 1
 
0.1%
Other values (1121) 1121
99.1%
ValueCountFrequency (%)
-10.72476975 1
0.1%
-10.65967953 1
0.1%
-9.970262098 1
0.1%
-9.831678296 1
0.1%
-9.825102182 1
0.1%
-9.824642409 1
0.1%
-9.824574766 1
0.1%
-9.733171954 1
0.1%
-9.603661067 1
0.1%
-9.56906362 1
0.1%
ValueCountFrequency (%)
73.16958797 1
0.1%
69.48593769 1
0.1%
59.93197387 1
0.1%
58.10524584 1
0.1%
50.54394309 1
0.1%
47.31817615 1
0.1%
45.75334767 1
0.1%
42.73966105 1
0.1%
41.89130689 1
0.1%
41.34107902 1
0.1%

battery_capacity_kwh
Real number (ℝ)

Distinct122
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.427818
Minimum1.5365397
Maximum193.00307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-12-21T09:57:15.001280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.5365397
5-th percentile50
Q162
median75
Q385
95-th percentile100
Maximum193.00307
Range191.46653
Interquartile range (IQR)23

Descriptive statistics

Standard deviation20.82835
Coefficient of variation (CV)0.2798463
Kurtosis2.2117991
Mean74.427818
Median Absolute Deviation (MAD)13
Skewness0.36103075
Sum84177.863
Variance433.82015
MonotonicityNot monotonic
2024-12-21T09:57:15.042335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 209
18.5%
50 205
18.1%
75 204
18.0%
62 200
17.7%
85 196
17.3%
108.4630074 1
 
0.1%
58.33515982 1
 
0.1%
96.60872071 1
 
0.1%
73.85002716 1
 
0.1%
53.97365866 1
 
0.1%
Other values (112) 112
9.9%
ValueCountFrequency (%)
1.536539743 1
0.1%
3.838518074 1
0.1%
3.976596499 1
0.1%
5.189529952 1
0.1%
6.168895844 1
0.1%
7.451954887 1
0.1%
7.714516854 1
0.1%
10.18928677 1
0.1%
12.03333697 1
0.1%
15.42168148 1
0.1%
ValueCountFrequency (%)
193.0030739 1
0.1%
188.6349646 1
0.1%
179.0130578 1
0.1%
157.5757789 1
0.1%
147.3953543 1
0.1%
143.4752097 1
0.1%
140.7620616 1
0.1%
140.5136826 1
0.1%
129.5241594 1
0.1%
129.3506162 1
0.1%

Interactions

2024-12-21T09:57:12.508638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.733400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.030221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.341025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.643096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.994079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.283251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.574726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.878643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.234372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.538904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.764872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.060680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.370590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.672869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.022847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.312873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.603857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.908711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.260946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.570617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.794863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.092191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.401741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.703553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.053063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.343734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.635055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.939977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.290021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.601883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.824939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.124815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.431968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.785351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.082862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.373723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.664957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.971170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.317869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.632981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.854389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.156816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.462456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.815624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.111419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.402832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.695659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.002336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.346371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.662348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.882389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.187916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.490699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.844271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.138135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.429738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.722834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.030792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.371910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.691752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.910795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.218135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.519742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.873460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.165879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.458242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.752465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.114246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.399833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.722649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.941067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.250003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.551372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.904604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.196036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.487393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.781969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.144566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.427110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.754346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.971368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.282222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.583869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.936613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.226774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.518750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.813586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.176013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.455751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.781202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:09.998261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.309227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.611289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:10.962780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.252144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.543806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:11.846330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.202429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-21T09:57:12.479432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-21T09:57:15.074874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
battery_capacity_kwhcharger_typecharging_cost_usdcharging_rate_kwday_of_weekdistance_driven_kmduration_hoursenergy_consumed_kwhsoc_end_percentsoc_start_percentstation_locationtemperature_ctime_of_dayuser_typevehicle_age_yearsvehicle_model
battery_capacity_kwh1.0000.000-0.009-0.0040.029-0.039-0.0300.0190.005-0.0700.000-0.0120.0000.0000.0410.000
charger_type0.0001.0000.0000.0000.0270.0000.0630.0230.0000.0770.0780.0040.0000.0290.0000.000
charging_cost_usd-0.0090.0001.0000.0050.0000.0100.005-0.017-0.063-0.0320.0390.0540.0360.000-0.0240.070
charging_rate_kw-0.0040.0000.0051.0000.0000.018-0.004-0.038-0.024-0.0340.0000.0060.0240.039-0.0530.031
day_of_week0.0290.0270.0000.0001.0000.0110.0000.0000.0460.0000.0000.0000.0000.0000.0430.053
distance_driven_km-0.0390.0000.0100.0180.0111.0000.025-0.0320.027-0.0200.000-0.0170.0000.076-0.0230.000
duration_hours-0.0300.0630.005-0.0040.0000.0251.0000.050-0.012-0.0200.025-0.0580.0430.0000.0310.028
energy_consumed_kwh0.0190.023-0.017-0.0380.000-0.0320.0501.0000.021-0.0150.000-0.0220.0620.0000.0030.000
soc_end_percent0.0050.000-0.063-0.0240.0460.027-0.0120.0211.000-0.0410.0300.0120.0000.0570.0220.025
soc_start_percent-0.0700.077-0.032-0.0340.000-0.020-0.020-0.015-0.0411.0000.0160.0140.0070.0000.0160.000
station_location0.0000.0780.0390.0000.0000.0000.0250.0000.0300.0161.0000.0000.0000.0580.0000.054
temperature_c-0.0120.0040.0540.0060.000-0.017-0.058-0.0220.0120.0140.0001.0000.0240.0430.0080.000
time_of_day0.0000.0000.0360.0240.0000.0000.0430.0620.0000.0070.0000.0241.0000.0000.0000.000
user_type0.0000.0290.0000.0390.0000.0760.0000.0000.0570.0000.0580.0430.0001.0000.0520.000
vehicle_age_years0.0410.000-0.024-0.0530.043-0.0230.0310.0030.0220.0160.0000.0080.0000.0521.0000.000
vehicle_model0.0000.0000.0700.0310.0530.0000.0280.0000.0250.0000.0540.0000.0000.0000.0001.000

Missing values

2024-12-21T09:57:12.829710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-21T09:57:12.912755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_iduser_typevehicle_modelvehicle_age_yearsstation_idstation_locationcharger_typestart_timeend_timeduration_hoursenergy_consumed_kwhcharging_cost_usdcharging_rate_kwsoc_start_percentsoc_end_percenttime_of_dayday_of_weekdistance_driven_kmtemperature_cbattery_capacity_kwh
0User_1CommuterBMW i32.000000Station_391HoustonDC Fast Charger2024-01-01 00:00:002024-01-01 00:39:000.59136360.71234613.08771736.38918129.37157686.119962EveningTuesday293.60211127.947953108.463007
1User_2Casual DriverHyundai Kona3.000000Station_428San FranciscoLevel 12024-01-01 01:00:002024-01-01 03:01:003.13365212.33927521.12844830.67773510.11577884.664344MorningMonday112.11280414.311026100.000000
2User_3CommuterChevy Bolt2.000000Station_181San FranciscoLevel 22024-01-01 02:00:002024-01-01 04:48:002.45265319.12887635.66727027.5135936.85460469.917615MorningThursday71.79925321.00200275.000000
3User_4Long-Distance TravelerHyundai Kona1.000000Station_327HoustonLevel 12024-01-01 03:00:002024-01-01 06:42:001.26643179.45782413.03623932.88287083.12000399.624328EveningSaturday199.57778538.31631350.000000
4User_5Long-Distance TravelerHyundai Kona1.000000Station_108Los AngelesLevel 12024-01-01 04:00:002024-01-01 05:46:002.01976519.62910410.16147110.21571254.25895063.743786MorningSaturday203.661847-7.83419950.000000
5User_6Long-Distance TravelerNissan Leaf0.000000Station_335San FranciscoDC Fast Charger2024-01-01 05:00:002024-01-01 07:10:001.16764043.18113736.90034114.33452375.21774871.982288EveningSaturday143.680046-5.27421850.000000
6User_7CommuterChevy Bolt4.000000Station_162HoustonLevel 22024-01-01 06:00:002024-01-01 07:53:003.53961936.86214022.21422526.18518860.75178170.796097EveningFriday81.33800927.55133585.000000
7User_8Long-Distance TravelerChevy Bolt0.000000Station_302Los AngelesLevel 22024-01-01 07:00:002024-01-01 10:42:002.65539651.4676179.79682126.70290856.20170363.786815AfternoonMonday116.543166-4.41746075.000000
8User_9CommuterChevy Bolt4.000000Station_493Los AngelesLevel 12024-01-01 08:00:002024-01-01 09:21:001.72420443.59237232.46500514.29492333.46620092.961421EveningWednesday208.25974222.51670662.000000
9User_10CommuterHyundai Kona2.830381Station_452ChicagoDC Fast Charger2024-01-01 09:00:002024-01-01 12:44:002.02687578.86860721.31230211.76100027.39945570.053381MorningWednesday54.00630927.51201950.000000
user_iduser_typevehicle_modelvehicle_age_yearsstation_idstation_locationcharger_typestart_timeend_timeduration_hoursenergy_consumed_kwhcharging_cost_usdcharging_rate_kwsoc_start_percentsoc_end_percenttime_of_dayday_of_weekdistance_driven_kmtemperature_cbattery_capacity_kwh
1121User_1309CommuterBMW i32.0Station_181San FranciscoLevel 22024-02-24 12:00:002024-02-24 13:57:000.9302396.43232416.4311019.02593971.49380573.286593NightFriday209.98356421.69695175.000000
1122User_1310Long-Distance TravelerHyundai Kona5.0Station_493Los AngelesLevel 12024-02-24 13:00:002024-02-24 14:57:003.90961116.12609027.6369954.19419761.13125271.022775AfternoonThursday82.88261229.23940050.000000
1123User_1312CommuterHyundai Kona3.0Station_13HoustonDC Fast Charger2024-02-24 15:00:002024-02-24 18:53:002.84915174.27670138.19400625.07354441.34406250.412341NightTuesday194.7935447.73196750.000000
1124User_1314Long-Distance TravelerTesla Model 33.0Station_458ChicagoDC Fast Charger2024-02-24 17:00:002024-02-24 19:35:002.36599160.72514411.28973310.49192141.85682594.097883NightSaturday279.552278-1.369994129.350616
1125User_1315Long-Distance TravelerHyundai Kona5.0Station_353Los AngelesLevel 22024-02-24 18:00:002024-02-24 19:58:002.50180943.25145336.35693063.21611862.10867265.198895MorningMonday220.2818631.63085050.000000
1126User_1316CommuterNissan Leaf7.0Station_57New YorkLevel 22024-02-24 19:00:002024-02-24 20:30:001.42644442.01165422.0811645.89547539.20410283.915952EveningSunday239.6010751.919655100.000000
1127User_1317Casual DriverBMW i34.0Station_40New YorkLevel 12024-02-24 20:00:002024-02-24 20:44:003.23821268.1858535.06780618.38801231.45637593.096461EveningTuesday164.37602234.029775100.000000
1128User_1318CommuterNissan Leaf5.0Station_374New YorkDC Fast Charger2024-02-24 21:00:002024-02-24 23:03:003.26712218.89510237.25500245.48206671.90308178.678879EveningTuesday226.51925820.358761100.000000
1129User_1319CommuterChevy Bolt5.0Station_336ChicagoDC Fast Charger2024-02-24 22:00:002024-02-24 23:20:002.75452713.75625239.04614638.14818376.18799765.926573AfternoonSunday291.49407624.13459885.000000
1130User_1320CommuterNissan Leaf5.0Station_128San FranciscoLevel 12024-02-24 23:00:002024-02-24 23:56:003.74097063.65257010.86367433.70422659.33807656.692439EveningMonday14.449236-6.966593120.447195